Published Paper Details:

Tropical cyclones (TCs) stand as dynamic and complex atmosphere-sea interaction phenomena, their behavior contingent upon a delicate interplay of oceanic and atmospheric conditions. Their emergence, dissipation, or intensification presents a challenge for accurate prediction, and as such, the development of precise diagnostic models holds immense potential for saving lives and safeguarding property. Existing techniques for diagnosing tropical cyclone wind speeds have displayed varying degrees of success, often limited by their reliance on specific points in time and satellite data. This paper introduces a groundbreaking paradigm shift by presenting a deeplearning-based objective diagnostic estimation of tropical cyclone intensity. Leveraging the power of deep learning, the model promises to transcend the limitations of traditional methods, offering a more nuanced and accurate representation of cyclonic behavior. A key innovation lies in the integration of an infrared satellite imagery-based diagnostic system. This method represents a new visualization gateway in addition to improving intensity estimation accuracy. This portal is not merely a scientific tool; it stands as one of the first systems to seamlessly translate deep learning results into a user-friendly interface, presenting not only raw data but also contextual information to end users. This move towards user accessibility marks a significant stride in bridging the gap between advanced scientific methodologies and practical, realworld applications